The main contribution of this paper is an approach to integrate semantics into a surfel-based map representation and a method to filter dynamic objects exploiting these semantic labels.
In sum, we claim that we are (i) able to accurately map an environment especially in situations with a large number of moving objects and we are (ii) able to achieve a better performance than the same mapping system simply removing possibly moving objects in general environments, including urban, countryside, and highway scenes.
We experimentally evaluated our approach on challenging sequences of KITTI [1]} and show superior performance of our semantic surfel-mapping approach, called SuMa++,
compared to purely geometric surfel-based mapping and compared mapping removing all potentially moving objects based on class labels. The source code of our approach is available at:
